Create utils.py

utils.py

@@ -0,0 +1,93 @@
+# https://huggingface.co/HannahLin271/nanoGPT_single_conversation/resolve/main/pytorch_model.bin
+import os
+import torch
+from model import GPTConfig, GPT
+from huggingface_hub import hf_hub_download
+import shutil
+import re
+import sys
+out_dir = "./out"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+import requests
+from pathlib import Path
+from tqdm import tqdm
+import gradio as gr
+
+def download_file(url, output_path):
+    response = requests.get(url, stream=True)
+    response.raise_for_status() 
+
+    total_size = int(response.headers.get("content-length", 0))
+    block_size = 1024 
+
+    # Create a progress bar
+    progress_bar = tqdm(total=total_size, unit="iB", unit_scale=True)
+
+    with open(output_path, "wb") as file:
+        for chunk in response.iter_content(chunk_size=block_size):
+            progress_bar.update(len(chunk))  
+            file.write(chunk) 
+
+    progress_bar.close()
+
+    if total_size != 0 and progress_bar.n != total_size:
+        print("Error: Downloaded file size does not match expected size")
+    else:
+        print(f"Download complete: {output_path}")
+  
+    try:
+        # Send a GET request to the URL
+        response = requests.get(url, stream=True)
+        response.raise_for_status()  # Check if the request was successful
+        if not os.path.exists(output_path):
+            print("downloading...")
+            output_path = Path(output_path)
+            output_path.parent.mkdir(parents=True, exist_ok=True)
+            with open(output_path, "wb") as file:
+                for chunk in response.iter_content(chunk_size=8192):
+                    file.write(chunk)
+
+        print(f"File downloaded successfully and saved as {output_path}")
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+
+def init_model_from(url, filename):
+    # if file not exists, download
+    ckpt_path = Path(out_dir) / filename
+    ckpt_path.parent.mkdir(parents=True, exist_ok=True)
+    if not os.path.exists(ckpt_path):
+        gr.Info('Downloading model...')
+        download_file(url, ckpt_path)
+        gr.Info('✅Model downloaded successfully.', duration=2)
+    checkpoint = torch.load(ckpt_path, map_location=device)
+    gptconf = GPTConfig(**checkpoint['model_args'])
+    model = GPT(gptconf)
+    state_dict = checkpoint['model']
+    unwanted_prefix = '_orig_mod.'
+    for k,v in list(state_dict.items()):
+        if k.startswith(unwanted_prefix):
+            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+    model.load_state_dict(state_dict)
+    return model
+
+def respond(input, samples, model, encode, decode, max_new_tokens,temperature, top_k): # generation function
+    x = (torch.tensor(encode(input), dtype=torch.long, device=device)[None, ...]) 
+    with torch.no_grad():
+        for k in range(samples):
+            generated = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
+
+            output = decode(generated[0].tolist())   
+
+            match_botoutput = re.search(r'<human>(.*?)<', output)
+            match_emotion = re.search(r'<emotion>\s*(.*?)\s*<', output)
+            match_context = re.search(r'<context>\s*(.*?)\s*<', output)
+            response = ''
+            emotion = ''
+            context = ''
+            if match_botoutput:
+                try :
+                    response = match_botoutput.group(1).replace('<endOfText>','')
+                except:
+                    response = match_botoutput.group(1)
+            #return response, emotion, context
+            return [input, response]